DGAM: Dual-Guided Anomaly Mining for Semi-Supervised Graph Anomaly Detection

For the challenging scenario in which only normal node labels are available in semi-supervised graph anomaly detection, existing generative methods usually synthesize abnormal nodes through random perturbation or feature interpolation. However, these methods fail to consider node abnormality compreh...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Xingxuan Li, Ting Guo, Zhen Tian
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2026-05-01
Sarja:	Information
Aiheet:	graph neural network graph anomaly detection semi-supervised graph anomaly detection
Linkit:	https://www.mdpi.com/2078-2489/17/6/521
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